CVPR 2023 - Learning Generative Structure Prior for Blind Text Image Super-resolution

In this episode we discuss Learning Generative Structure Prior for Blind Text Image Super-resolution by Xiaoming Li, Wangmeng Zuo, Chen Change Loy. This paper proposes a novel prior for blind text image super-resolution (SR), focusing on character structure, which can deal with diverse font styles and unknown degradation. The authors store discrete features for each character in a codebook to drive a StyleGAN to generate high-resolution structural details that aid text SR. The proposed structure prior exerts stronger character-specific guidance than previous methods based on character recognition, resulting in compelling performance on synthetic and real datasets. The code for the proposed approach is available on GitHub.